Real-time model-based video stabilization for microaerial vehicles

The emerging branch of micro aerial vehicles (MAVs) has attracted a great interest for their indoor navigation capabilities, but they require a high quality video for tele-operated or autonomous tasks. A common problem of on-board video quality is the effect of undesired movements, so different approaches solve it with both mechanical stabilizers or video stabilizer software. Very few video stabilizer algorithms in the literature can be applied in real-time but they do not discriminate at all between intentional movements of the tele-operator and undesired ones. In this paper, a novel technique is introduced for real-time video stabilization with low computational cost, without generating false movements or decreasing the performance of the stabilized video sequence. Our proposal uses a combination of geometric transformations and outliers rejection to obtain a robust inter-frame motion estimation, and a Kalman filter based on an ANN learned model of the MAV that includes the control action for motion intention estimation.Peer ReviewedPostprint (author's final draft

3D environment mapping using the Kinect V2 and path planning based on RRT algorithms

This paper describes a 3D path planning system that is able to provide a solution trajectory for the automatic control of a robot. The proposed system uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Our proposal includes a collision-free path planner based on the Rapidly-exploring Random Trees variant (RRT*), for a safe and optimal navigation of robots in 3D spaces. Results on RGB-D segmentation and recognition, point cloud processing, and comparisons between different RRT* algorithms, are presented.Peer ReviewedPostprint (published version

Estabilización de vídeo en tiempo real : aplicaciones en teleoperación de micro vehículos aéreos de ala rotativa

Micro Aerial Vehicles (MAVs), a subset of Unmanned Aerial Vehicles (UAVs), also known as drones, are becoming popular for several applications and gaining interest due to advantages as manufacturing and maintenance cost, size and weight, energy consumption, and flight maneuverability. Required skills for drone teleoperators being lower than for aircraft pilots, however their training process can last several weeks or months depending on the target at hands. In particular, this process is harder when teleoperators cannot observe directly the vehicle, depending only on onboard sensors and cameras. The presence of oscillations in the captured video is a major problem with cameras on UAVs. It is even more complex for MAVs because the external disturbances increase the instability. There exists mechanical video stabilizers that reduce camera oscillations, however this mechanical device adds weight and increases the manufacturing cost, energy consumption, size, weight, and the system becomes less safe for people. In this thesis, we propose to develop video stabilization software algorithms, without additional mechanical elements in the system, to be applied in real-time during the UAV navigation. In the literature, there are a few video stabilization algorithms able to be applied in real-time, but most of them generate false motion (phantom movements) in the stabilized image. Our algorithm represents a good tradeoff between stable video recording and simultaneously keeping UAV real motion. Several experiments with MAVs have been performed and the employed measurements demonstrate the good performance of the introduced algorithm.Los micro vehículos aéreos (MAVs), un subconjunto de vehículos aéreos no tripulados (UAVs), también llamados drones, han ganado popularidad en múltiples aplicaciones y un creciente interés debido a sus ventajas como costo de fabricación y mantenimiento, volumen, peso del vehículo, gasto energético, y maniobrabilidad de vuelo. La destreza requerida para un teleoperador de drones es inferior a la de un piloto de aeronaves de mayor dimensión, no obstante, su proceso de entrenamiento puede durar varias semanas o incluso meses dependiendo del objetivo que se persiga. Este proceso se dificulta cuando el teleoperador no puede observar de forma directa al vehículo y depende únicamente de los sensores y cámaras a bordo del sistema. Uno de los principales problemas con cámaras a bordo de drones es la oscilación presente en los vídeos capturados. Este inconveniente es más complejo para los MAVs porque las perturbaciones externas provocan mayor inestabilidad. Existen dispositivos mecánicos de estabilización de vídeo que reducen las oscilaciones en la cámara. Sin embargo, estos mecanismos implican una carga adicional al sistema y aumentan el costo de producción, gasto energético y el riesgo para las personas que se encuentren cerca en caso de accidente. En la presente tesis se propone el desarrollo de algoritmos de estabilización de vídeo por software sin elementos mecánicos adicionales en el sistema, a ser utilizados en tiempo real durante la navegación de los UAVs. En la literatura existen pocos algoritmos de estabilización de video aplicables en tiempo real, los cuales generan falsos movimientos (movimientos fantasma) en la imagen estabilizada. El algoritmo desarrollado es capaz de obtener una imagen estable y simultáneamente mantener los movimientos reales. Se han llevado a cabo múltiples experimentos con MAVs y las métricas de evaluación utilizadas evidencian el buen desempeño del algoritmo introducido

Real-time video stabilization without phantom movements for micro aerial vehicles

In recent times, micro aerial vehicles (MAVs) are becoming popular for several applications as rescue, surveillance, mapping, etc. Undesired motion between consecutive frames is a problem in a video recorded by MAVs. There are different approaches, applied in video post-processing, to solve this issue. However, there are only few algorithms able to be applied in real time. An additional and critical problem is the presence of false movements in the stabilized video. In this paper, we present a new approach of video stabilization which can be used in real time without generating false movements. Our proposal uses a combination of a low-pass filter and control action information to estimate the motion intention.Peer ReviewedPostprint (published version

Obstacle avoidance based-visual navigation for micro aerial vehicles

This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.Peer ReviewedPostprint (published version

Real-time video stabilization without phantom movements for micro aerial vehicles

In recent times, micro aerial vehicles (MAVs) are becoming popular for several applications as rescue, surveillance, mapping, etc. Undesired motion between consecutive frames is a problem in a video recorded by MAVs. There are different approaches, applied in video post-processing, to solve this issue. However, there are only few algorithms able to be applied in real time. An additional and critical problem is the presence of false movements in the stabilized video. In this paper, we present a new approach of video stabilization which can be used in real time without generating false movements. Our proposal uses a combination of a low-pass filter and control action information to estimate the motion intention.Peer Reviewe

Obstacle avoidance based-visual navigation for micro aerial vehicles

This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.Peer Reviewe